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--- |
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title: Cifar10 |
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emoji: 🔥 |
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colorFrom: purple |
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colorTo: indigo |
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sdk: gradio |
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sdk_version: 4.38.1 |
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app_file: app.py |
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fullWidth: true |
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models: |
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- >- |
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https://huggingface.co/spaces/Shilpaj/cifar10/blob/main/epoch%3D23-step%3D2112.ckpt |
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datasets: |
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- CIFAR10 |
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pinned: false |
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license: mit |
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--- |
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# Model Trained for CIFAR10 |
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- This Application demonstrate the inference side of the model trained on the CIFAR dataset |
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- David C's model architecture is recreated and trained on CIFAR10 dataset to achieve the accuracy of 90+% within 24 epochs |
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- Once Cycle Policy is used to speed up the training process |
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- The model is coded using PyTorch Lightning. Mentioned below is the link for Training Repository where you can check the code for model training and tracked metrics |
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##### [Training Repo Link](https://github.com/Shilpaj1994/ERA/tree/master/Session12) |
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- After the Training, model checkpoint is stored on the system and uploaded to Gradio Spaces. Attached below is the link to download model file |
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##### [Download Model File](https://huggingface.co/spaces/Shilpaj/cifar10/resolve/main/epoch%3D23-step%3D2112.ckpt) |
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- This app has four features in four tabs: |
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- **GradCam:** |
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- To visualize which portion of the image model is actually looking at while inferencing on the image |
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- Using this, we can come up with an augmentation strategy that can improve the model accuracy |
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- **Misclassified Image:** |
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- While training the model, though the `test accuracy` was 90+%, there were still 10% images which were misclassified |
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- The feature helps to visualize those images with their respective correct and incorrect labels |
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- This can be used to come up with a strategy to improve accuracy for a particular class |
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- **Feature Map Visualization:** |
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- There are 6 block in this model |
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- Each block has 2 or 3 convolutional layers |
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- The output of specific kernel are visualized for first convolutional layer of all 6 blocks |
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- **Kernel Visualization:** |
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- In the first layer of each of the six blocks, there are kernels which are creating the feature maps |
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- Some of those kernels are visualized in this section |
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**Dependencies:** |
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- Python Version: 3.x |
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- PyTorch Lightning: 2.0.6 |
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## Usage: |
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### GradCam |
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- Upload an image or select from example |
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- Select how much percentage of original image should be overlapped on what actually model is looking at in the image |
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- Select the number of top classes you want to see |
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- Select the block number for which first convolutional layer's activation you want to see |
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- Click on the `Submit` button to see the results |
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### Misclassified Images |
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- Select the number of misclassified images you want to see |
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- Click on `Display Misclassified Images` to show the images in the center and their respective correct and misclassified labels in the sequence |
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### Feature Map Visualization |
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- Upload an image |
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- Select the kernel number |
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- Click on `Visualize FeatureMaps` to see the feature maps created by the kernel number for first convolutional layer in each of the six blocks |
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### Kernel Visualization |
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- Select the block number from which first convolutional layer's kernel to be visualized |
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- Click on `Visualize Kernels` to see the kernels |
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